{"id":"W4376138902","doi":"10.1093/forestry/cpad025","title":"Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data","year":2023,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Forest Service","funders":"Canadian Forest Service; U.S. Forest Service; Norges Forskningsråd","keywords":"Statistics; Laser scanning; Plot (graphics); Forest inventory; Forest plot; Environmental science; Tree (set theory); Site index; Logistic regression; Mathematics; Forest management; Remote sensing; Forestry; Geography; Biology; Laser","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001245328,0.0003921452,0.0004179595,0.001001339,0.0004858526,0.001270985,0.0005751895,0.0002963883,0.001055504],"category_scores_gemma":[0.004798689,0.0002302537,0.0003597522,0.001084704,0.0003067501,0.0008782914,0.0005297703,0.0003022625,0.0004835128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006972217,"about_ca_system_score_gemma":0.0009579198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1322009,"about_ca_topic_score_gemma":0.2375675,"domain_scores_codex":[0.9994259,0.0001160166,0.00004483776,0.0001674081,0.0001563626,0.00008957658],"domain_scores_gemma":[0.9972295,0.001294593,0.0004458429,0.0002217765,0.0006682222,0.0001400725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001643501,0.00009838212,0.9136981,0.00008058182,0.00006080831,0.00008148933,0.0002678405,0.01568334,0.008266678,0.0000964704,0.0004646983,0.06103724],"study_design_scores_gemma":[0.00001157868,0.00009293148,0.8305877,0.00002799521,0.00004201176,0.0001028636,0.0004435364,0.1648893,0.002934596,0.0001339822,0.000703181,0.00003013274],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827566,0.00008679854,0.01527006,0.00002274393,0.000005114512,0.00003157807,0.0003711028,0.0002298291,0.001226105],"genre_scores_gemma":[0.9891384,0.00004115257,0.009849935,0.000009129778,0.00000231146,0.00001159349,0.0006536928,0.0000129846,0.0002807294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1322009,"threshold_uncertainty_score":0.2628627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0995582593928272,"score_gpt":0.3951454653431296,"score_spread":0.2955872059503024,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}